Download e-book for iPad: Advances in Intelligent Data Analysis VIII: 8th by Paul Cohen, Niall Adams (auth.), Niall M. Adams, Céline

By Paul Cohen, Niall Adams (auth.), Niall M. Adams, Céline Robardet, Arno Siebes, Jean-François Boulicaut (eds.)

ISBN-10: 3642039146

ISBN-13: 9783642039140

ISBN-10: 3642039154

ISBN-13: 9783642039157

This publication constitutes the refereed court cases of the eighth foreign convention on clever information research, IDA 2009, held in Lyon, France, August 31 – September 2, 2009.

The 33 revised papers, 18 complete oral shows and 15 poster and brief oral shows, provided have been conscientiously reviewed and chosen from virtually eighty submissions. All present features of this interdisciplinary box are addressed; for instance interactive instruments to steer and help information research in advanced eventualities, expanding availability of instantly accrued information, instruments that intelligently aid and support human analysts, the best way to keep watch over clustering effects and isotonic type bushes. commonly the parts coated contain information, computing device studying, information mining, category and development acceptance, clustering, functions, modeling, and interactive dynamic info visualization.

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Extra resources for Advances in Intelligent Data Analysis VIII: 8th International Symposium on Intelligent Data Analysis, IDA 2009, Lyon, France, August 31 - September 2, 2009. Proceedings

Example text

In this paper, we propose a statistical approach to detecting distributional shifts in multi-dimensional data streams. We use relative entropy, also known as the Kullback-Leibler distance, to measure the statistical distance between two distributions. In the context of a multidimensional data stream, the distributions are generated by data from two sliding windows. We maintain a sample of the data from the stream inside the windows to build the distributions. Our algorithm is streaming, nonparametric, and requires no distributional or model assumptions.

MBLearning is then first run on the target variable T (line 2). It is then run again repeatedly on the adjacent nodes and so on up to a radius of r around the target node (lines 5-13). A similar approach was proposed in [19], but it does not take into account missingness as a possible piece of information. After finishing the feature subset selection process, GMB creates at line 14 the local BN including the selection of existing and the dummy variables. The user-defined radius of the Bayesian network constructed by GMB (r) trades off accuracy and scalability.

Adams et al. ): IDA 2009, LNCS 5772, pp. 35–46, 2009. c Springer-Verlag Berlin Heidelberg 2009 36 S. Rodrigues de Morais and A. Aussem inaccessible factors. Thus, although a researcher may not be confident that the data present a purely accessible mechanism, covering as much of the mechanism as possible should be regarded as beneficial rather than detrimental. In this study, we experiment a new graphical method of treating missing values, based on Bayesian networks (BN). We describe a novel approach that uses explicitly the information represented by the absence of data to help detect the missing mechanism and reduce the classification error.

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Advances in Intelligent Data Analysis VIII: 8th International Symposium on Intelligent Data Analysis, IDA 2009, Lyon, France, August 31 - September 2, 2009. Proceedings by Paul Cohen, Niall Adams (auth.), Niall M. Adams, Céline Robardet, Arno Siebes, Jean-François Boulicaut (eds.)


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